【正文】 基本概念是多值之間的相似的對象 ,而不是一種概括規(guī)則的推理方式 ,Ponens 經(jīng)典。結果對兩相控制和 PappisMamdani 控制表明 ,模糊控制應用領域很廣。這 FUSICOproject 仍在繼續(xù)。 ３ ? influence mode and route choice。 and ? improve safety for all road user groups. In adaptive traffic signal control the increase in flexibility increases the number of overlapping green phases in the cycle, thus making the mathematical optimization very plicated and difficult. For that reason, the adaptive signal control in most cases is not based on precise optimization but on the green extension principle. In practice, uniformity is the principle followed in signal control for traffic safety reasons. This sets limitations to the cycle time and phase arrangements. Hence, traffic signal control in practice are based on tailormade solutions and adjustments made by the traffic planners. The modern programmable signal controllers with a great number of adjustable parameters are well suited to this process. For good results, an experienced planner and finetuning in the field is needed. Fuzzy control has proven to be successful in problems where exact mathematical modelling is hard or impossible but an experienced human can control the process operator. Thus, traffic signal control in particular is a suitable task for fuzzy control. Indeed, one of the oldest examples of the potentials of fuzzy control is a simulation of traffic signal control in an intersection of two oneway streets. Even in this very simple case the fuzzy control was at least as good as the traditional adaptive control. In general, fuzzy control is found to be superior in plex problems with multiobjective decisions. In traffic signal control several traffic flows pete from the same time and space, and different priorities are often set to different traffic flows or vehicle groups. In addition, the optimization includes several simultaneous criteria, like the average and maximum vehicle and pedestrian delays, maximum queue lengths and percentage of stopped vehicles. So, it is very likely that fuzzy control is very petitive in plicated real intersections where the use of traditional optimization methods is problematic. Fuzzy logic has been introduced and successfully applied to a wide range of automatic control tasks. The main benefit of fuzzy logic is the opportunity to model the ambiguity and the uncertainty of decisionmaking. Moreover, fuzzy logic has the ability to prehend linguistic instructions and to generate control strategies based on priori munication. The point in utilizing fuzzy logic in control theory is to model control based on human expert knowledge, rather than to model the process itself. Indeed, fuzzy control has proven to be successful in problems where exact mathematical modelling is hard or impossible but an experienced human operator can control process. In general, fuzzy control is found to be superior in plex problems with multiobjective decisions. ４ At present, there is a multitude of inference systems based on fuzzy technique. Most of them, however, suffer illdefined foundations。. Taking this remark seriously, we study systematically manyvalued equivalence, . fuzzy similarity. It turns out that, starting from the Lukasiewicz welldefined manyvalued logic, we are able to construct a method performing fuzzy reasoning such that the inference relies only on experts knowledge and on welldefined logical concepts. Therefore we do not need any artificial defuzzification method (like Center of Gravity) to determine the final output of the infere